Vision Transformer (ViT) Base Model
| Property | Value |
|---|---|
| Parameter Count | 86.4M |
| License | Apache 2.0 |
| Training Dataset | ImageNet-21k |
| Paper | Original Paper |
| Framework Support | PyTorch, JAX |
What is vit-base-patch16-224-in21k?
The vit-base-patch16-224-in21k is a Vision Transformer model developed by Google that represents a fundamental shift in computer vision approaches. This model processes images by dividing them into 16x16 pixel patches and treating them as a sequence of tokens, similar to how traditional transformers process text.
Implementation Details
The model was trained on ImageNet-21k, comprising 14 million images across 21,843 classes. It operates at a 224x224 pixel resolution and employs a transformer encoder architecture with a specialized patch embedding system.
- Pre-processes images into 16x16 pixel patches
- Includes a [CLS] token for classification tasks
- Uses absolute position embeddings
- Trained with batch size 4096 and learning rate warmup of 10k steps
Core Capabilities
- Image feature extraction and representation learning
- Support for downstream classification tasks
- Flexible integration with both PyTorch and JAX frameworks
- Pre-trained pooler for transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its transformer-based approach to computer vision, moving away from traditional convolutional architectures. It's pre-trained on the extensive ImageNet-21k dataset, making it particularly robust for transfer learning tasks.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, feature extraction, and as a backbone for various computer vision applications. It's particularly effective when fine-tuned on domain-specific datasets.
